When to use custom gradients?

Custom gradients in TensorFlow are used when you want to define a custom gradient for a TensorFlow operation. This can be useful in several scenarios:

1. Numerical Stability: Sometimes, the default gradient computation can lead to numerical instability. In such cases, you can define a custom gradient that provides a more stable computation.
2. Efficiency: Custom gradients can be used to provide a more efficient computation compared to the default gradients. This can be useful when the default computation is inefficient or when you have a more efficient way to compute the gradient.
3. Non-Differentiable Operations: If you have operations in your model that are not differentiable, you can use custom gradients to define a gradient for these operations.
4. Improved Performance: In some cases, using custom gradients can lead to improved performance of your model, either in terms of training speed or final performance metrics.
5. Research and Experimentation: Custom gradients can be used in research or experimentation to explore novel ideas or improve existing models.

Custom gradients in TensorFlow allow you to define your gradient functions for operations, providing flexibility in how gradients are computed for complex or non-standard operations. This can be useful for tasks such as implementing custom loss functions, incorporating domain-specific knowledge into the gradient computation, or handling operations that TensorFlow does not natively support.